Third Quartile Calculator
Calculate the third quartile (Q3), upper quartile, and 75th percentile of your data. Get complete five-number summary, IQR, and outlier detection for comprehensive statistical analysis.
Enter numeric values separated by commas
What is the third quartile (Q3)?
The third quartile (Q3), also called the upper quartile or 75th percentile, is the value below which 75% of the data falls. It marks the boundary between the third and fourth quarters of your dataset when sorted. Q3 is a measure of position in descriptive statistics.
How do I interpret the third quartile value?
Q3 tells you that 75% of your data is less than or equal to this value, and 25% is greater. It defines the upper boundary of the middle 50% of data (the box in a box plot). The range from Q3 to maximum shows where the top quarter of your data lies.
What is the difference between Q1, Q2, and Q3?
Q1 (25th percentile): 25% of data below, 75% above. Q2 (50th percentile/median): Half above, half below - the middle value. Q3 (75th percentile): 75% below, 25% above. Together they divide data into four equal parts. IQR = Q3 - Q1 measures middle 50% spread.
How is the third quartile calculated?
Method: (1) Sort data in ascending order, (2) Find position = 0.75 × (n+1), (3) If position is whole number, Q3 = that value, (4) If decimal, interpolate between neighboring values. Example: 8 values, position = 0.75×9 = 6.75, Q3 is between 6th and 7th values.
Why is Q3 important in box plots and outlier detection?
Q3 is crucial for: (1) Box plots - top of the box, (2) IQR calculation = Q3 - Q1, (3) Upper fence = Q3 + 1.5×IQR for outlier detection. Values above upper fence are potential outliers. Q3 is robust to extreme values, making it reliable for skewed data.